Method, device, and system to quantify waste at the bin-level

ABSTRACT

A method includes receiving data from a device, the data includes at least a first amount of a first type of waste. The method includes comparing the data with a profile including historical waste data from a location, and sending notifications to the device based on the comparing operation. The notifications include information relating to the data and the profile. The device communicates the notifications to a user. The method may include receiving first goals from the device, and determining second goals based on community waste averages. The profile may include the first goals and the second goals. The method may include sending recommendations to the device based at least in part on the data and the profile. An exemplary device includes at least one sensor associated with a waste receptacle, which may be a computer vision imaging sensor.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Provisional PatentApplication No. 63/309,947, filed Feb. 14, 2022, entitled “DEVICE TOQUANTIFY HOUSEHOLD WASTE AT THE BIN-LEVEL”, which is incorporated byreference in its entirety.

BACKGROUND 1. Field of the Invention

The present disclosure relates generally to embedded or standalonedevices and, more specifically, to devices for quantifying waste at aper-bin level.

2. Description of the Related Art

Waste generation and the subsequent accumulation thereof is a problem ona global scale. Most people are conceptually aware that the waste theygenerate can cause a negative environmental impact. Indeed, many peopledesire to generate less waste and some even attempt to make efforts toreduce waste. However, those people often rely on actions that theythink are best based on minimal information or incorrect assumptions. Inmany cases, the only feedback people receive on their actions is at ahigh-level (e.g., community, city or regional feedback) and long afterthe fact.

In some cases, public (or private) waste services may publish quarterlyor yearly waste metrics for a service area or region. Additionally, awaste service may publish a bulletin to inform users within a servicearea or region about procedures or recommendations for separating waste(e.g., recyclable or compostable materials from other trash). Thehigh-level metrics, even when provided to people within a service areaor region, are insufficient to inform people as to how following aprocedure or recommendation actively reduces the amount of waste theyindividually produce, and the impact of that waste on the environment.Moreover, the procedures or recommendations are often forgotten, or toogeneral, and in either case are disconnected from the impact of wasteproduced (or reduced) at the point in time when an individual chooseswhether to perform an action.

SUMMARY OF THE INVENTION

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

A method to continuously capture signal data from one or more sensors,process the measurements, and store results of the processing to amemory. The one or more sensors may include one or more weight sensors,computer vision imaging sensors, visual sensor, ultrasonic sensor,chemical sensor, or other sensors for determining properties about wastegenerated by a household. References to household in this applicationmay refer to any customer location, for example, an office, anyinstitution, a school, a hospital, a campus, etc. One or more sensorsmay be associated with a waste receptacle to capture signal data, likemeasurement data including one or more measurements, images, or thelike, indicative of the waste added to the receptacle. The sensors maytransmit captured signal data for processing, which in some examples maybe processed by a local device. For example, the local device maydetermine properties of waste added to a receptacle based on themeasurement data obtained from the one or more sensors associated withthe waste receptacle. In some examples, the local device may transmitinformation about the data obtained from the sensors or resultsdetermined based on processing the data obtained from the sensors to aremote server. Some embodiments may determine a profile, which may bereferred to herein as a household profile, and which may be a profile ofa household, an office, institution, school, hospital, campus or othercustomer location. The profile may be based on properties of waste addedover time to one or more receptacles tracked by the local device, toestimate the daily average to then provide feedback to the user abouttarget settings. The local device may communicate with the remote serverto update artificial intelligence and machine learning models by whichcaptured sensor data may be processed. In some examples, the models mayprocess other data, such as to determine target based goals, determinenotifications, and determine other feedback or recommendations based atleast in part on captured signal data and the household profile. Thelocal device or remote server may use data processing and statisticalanalysis models to classify waste, such as by properties determined frommeasurements like weight, volume, etc., by type/composition to determineamounts of different types of waste generated. In some examples, theremote server may store and update user profiles and determinenotifications or other feedback to inform a user or household about thewaste generated. In some examples, the notifications may inform a usertemporally proximate to the addition of waste placed in a receptacle asto whether the waste added to the receptacle should be placed (wholly orpartially) in another receptacle (e.g., for receiving a specifictype/composition of waste) to improve mis-categorization of wastedestined for bulk waste processing (e.g., recycling), or to indicate toa user that certain the waste is compostable.

Some aspects include a tangible, non-transitory, machine-readable mediumstoring instructions that when executed by a data processing apparatuscause the data processing apparatus to perform operations including theabove-mentioned process.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to effectuate operations of the above-mentioned process.

Exemplary embodiments of the present invention provide a method thatincludes receiving data from a device. The data includes at least afirst amount of a first type of waste. The method also includescomparing the data with a profile including historical waste data from alocation, and sending notifications to the device based on the comparingoperation. The notifications include information relating to the dataand the profile. The device is configured to communicate thenotifications to a user.

The method may include receiving first goals from the device, anddetermining second goals based on community waste averages. The profilemay include the first goals and the second goals.

In the method, the comparing may include comparing the first amount tothe first goal. The notifications may include information tracking afirst progress towards the first goal. The comparing may also includecomparing the first amount to the second goal. The notifications mayinclude information tracking a second progress towards the second goal.The notifications may include positive feedback prompts for wastereduction and/or awards for waste reduction.

The historical waste data may include a daily average, and the comparingoperation may include comparing the data and the daily average.

The method may include sending recommendations to the device based atleast in part on the data and the profile. The device may be configuredto communicate the notifications and the recommendations to the user byat least one of a display, a speaker, and a wireless connection to amobile device. The method may include sending the notifications to anapplication running on a mobile device of the user.

An exemplary device according to the present technology includes atleast one sensor associated with a waste receptacle. The at least onesensor is configured to determine properties of waste added to the wastereceptacle. The exemplary device includes a processor configured toreceive measurement data from the at least one sensor and configured toanalyze the measurement data. The exemplary device includes a display, aspeaker, and/or a wireless connection to a mobile device configured toprovide information to a user, the information being based on themeasurement data obtained from the at least one sensor associated withthe waste receptacle.

The information includes categorization notifications. Thecategorization notifications indicate whether waste placed in the wastereceptacle is properly placed wholly or partially in another receptacle,or compostable.

The device may be configured to transmit to a remote server themeasurement data from the at least one sensor, and/or a result from theprocessor analyzing the measurement data.

The at least one sensor may be configured to continuously capture themeasurement data and may be a weight sensor, a computer vision imagingsensor, a visual sensor, an ultrasonic sensor, and/or a chemical sensor.

The at least one sensor may be the computer vision imaging sensor, andthe computing vision imaging sensor may include a processor running anartificial intelligence trained on further measurement data obtainedfrom at least one further sensor associated with at least one furtherwaste receptacle.

An exemplary system is provided that includes a receiving moduleconfigured to receive data from a location. The data obtained from asensor measuring a first amount of a first type of waste. The exemplarysystem may also include a processor configured to receive the data fromthe receiving module and process the data, and a sending moduleconfigured to send notifications to a user device. The notifications mayinclude information relating to the first amount. The user device may beconfigured to display the notifications to a user.

The processor may be further configured to classify the waste based on astatistical analysis models. The data may be measurements of the waste,and the measurements may be a weight, a volume, and/or a composition.The processor may determine amounts of different types of wastegenerated.

The processor may be further configured to update a machine learningmodel for identifying the different types of waste generated based onthe measurements.

The receiving module is configured to receive second data from a secondlocation. The second data may be obtained from a second sensor measuringa second amount of the first type of waste. The processor may beconfigured to receive the second data from the receiving module andprocess the second data and compare the first data and the second data.The sending module may be configured to send second notifications to asecond user device. The second notifications may include furtherinformation relating to the second amount. The second user device may beconfigured to display the second notifications to a second user. Thenotification may further include an award, an indication of communitysupport, a first comparison of the first amount and a community wastemetric, and/or a second comparison of a daily average of the locationand a community daily average.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1 is an example computing environment for implementing a wastetracking system at the user or household level, in accordance with someembodiments;

FIGS. 2A and 2B are example configurations for implementing a wastetracking system at the user or household level, in accordance with someembodiments;

FIG. 3 illustrates example sensor configurations for tracking wasteadded to a waste receptacle at the user or household level, inaccordance with some embodiments;

FIG. 4 illustrates an example weight sensor device for capturing ameasurement of waste added to a waste receptacle at the user orhousehold level, in accordance with some embodiments;

FIG. 5 is an example machine learning model in accordance with someembodiments;

FIG. 6A is a flowchart of an example process for determining propertiesabout waste added to a waste receptacle to track waste generation, inaccordance with some example embodiments;

FIG. 6B is a flowchart of an example process for reporting on trackedwaste generation across one or more waste receptacles, in accordancewith some example embodiments;

FIGS. 7A and 7B illustrate an example user interface for registeringsensors in association with a receptacle for tracking waste generated,in accordance with some embodiments;

FIG. 7C illustrates example user interfaces for reporting propertiesdetermined about generated waste added to waste receptacles, inaccordance with some embodiments;

FIG. 8 is a physical architecture block diagram that shows an example ofa computing device (or data processing system) by which some aspects ofthe above techniques may be implemented.

FIG. 9 is a flowchart illustrating a method according to an exemplaryembodiment of the present invention;

While the present techniques are susceptible to various modificationsand alternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit thepresent techniques to the particular form disclosed, but to thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the presenttechniques as defined by the appended claims.

DETAILED DESCRIPTION

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the field ofwaste measurement at the individual or household level. Indeed, theinventors wish to emphasize the difficulty of recognizing those problemsthat are nascent and will become much more apparent in the future shouldtrends in industry continue as the inventors expect. Further, becausemultiple problems are addressed, it should be understood that someembodiments are problem-specific, and not all embodiments address everyproblem with traditional systems described herein or provide everybenefit described herein. That said, improvements that solve variouspermutations of these problems are described below.

Users wishing to reduce waste are confronted by a lack of individual andhousehold specific metrics that inform users about the waste theygenerate. While waste services may broadly track the amount of wastethey process, those metrics are too far removed from the individualusers of the service to inform a user about the waste they generate asthe individual or household level. A domestic waste tracking system isneeded help users become smarter about the waste they produce and helpthem reduce what they throw away, such as by identifying recyclable orcompostable waste.

Example embodiments of a household waste tracking system maycontinuously capture signal data from one or more sensors, process themeasurements, and store results of the processing to a memory. The oneor more sensors may include one or more weight sensors, computer visionimaging sensors, visual sensor, ultrasonic sensor, chemical sensor, orother sensors for determining properties about waste generated by ahousehold. One or more sensors may be associated with a waste receptacleto capture signal data, like measurement data including one or moremeasurements, images, or the like, indicative of the waste added to thereceptacle. The sensors may transmit captured signal data forprocessing, which in some examples may be processed by a local device.For example, the local device may determine properties of waste added toa receptacle based on the measurement data obtained from the one or moresensors associated with the waste receptacle. In some examples, thelocal device may transmit information about the data obtained from thesensors or results determined based on processing the data obtained fromthe sensors to a remote server. Some embodiments may determine ahousehold profile, such as based on properties of waste added over timeto one or more receptacle tracked by the local device, to estimate thedaily average to then provide feedback to the user about targetsettings. The local device may communicate with the remote server toupdate artificial intelligence and machine learning models by whichcaptured sensor data may be processed. In some examples, the models mayprocess other data, such as to determine target based goals, determinenotifications, and determine other feedback or recommendations based atleast in part on captured signal data and the household profile. Thelocal device or remote server may use data processing and statisticalanalysis models to classify waste, such as by properties determined frommeasurements like weight, volume, etc., by type/composition to determineamounts of different types of waste generated. In some examples, theremote server may store and update user profiles and determinenotifications or other feedback to inform a user or household about thewaste generated. In some examples, the notifications may inform a usertemporally proximate to the addition of waste placed in a receptacle asto whether the waste added to the receptacle should be placed (wholly orpartially) in another receptacle (e.g., for receiving a specifictype/composition of waste) to improve mis-categorization of wastedestined for bulk waste processing (e.g., recycling), or to indicate toa user that certain the waste is compostable.

FIG. 1 is an example computing environment 100 for implementing a wastetracking system at the user or household level, in accordance with someembodiments. The computing environment 100 may include one or moremeasurements systems 101, user devices 110, servers 105, and databases130. User device as used throughout this application, refers to anadd-on device for attaching or coupling in some manner to a bin, anintegrated bin utilizing the present technology, and/or an applicationrunning on a mobile device coupled to a one or both of the previouslydescribed devices. While only one remote server system 105, anddatabase, e.g., waste database 130, are shown, the server system 105 ordatabase may include multiple compute or storage servers or beimplemented by a distributed system including multiple compute orstorage nodes, and functionality or data stored may be distributedacross multiple ones of nodes or servers. Each of the measurement system101, server system 105, database 130, and user devices 110 (or othercomponents described herein) may communicate with one another (which isnot to suggest that a component need to communicate with every othercomponent) via a network 150, such as the internet, which may includepublic or private local area networks. Each of these computing devicesmay have the features of the computing system described below, includinga processor and memory. In some embodiments, the functionality describedherein may be implemented with program code or other instructions storedon a tangible, non-transitory, machine-readable medium, such that whenthat program code is executed by one or more processors, the describedfunctionality is effectuated.

Embodiments of the measurement system 101 may include a trainingsubsystem 104, an evaluation subsystem 106, and a sensor subsystem 108by which functionality of the measurement system 101 may be implemented.Functionality of these components or otherwise ascribed to themeasurement system 101 may be divided in different ways, in some casesamong different computing devices. For example, one or more of thesecomponents may be hosted on a remote server 105 supporting measurementsystem 101 functionality, or a server system implemented with aplurality of servers that each, or collectively, execute processes upondata or portions of data like that described herein. In some examples,the database 130 may be implemented within the context of theenvironment to track household waste at the bin level, such as by one ormore servers or storage servers by which functionalities of componentsof a remote server 105 are implemented, or separately, such as within acloud storage system, which the measurement system 101 or remote server105 may communicate with to store data and obtain stored data.

The measurement system 101, in some embodiments, may include a trainingsubsystem to train a model (e.g., during a startup training phase) whichmay be run to respond to novel inputs during runtime, and mayperiodically or continuously retrain the model. In some embodiments, thetraining subsystem 104 may report data to a remote server 105 and themeasurement system 101 may receive a model or updated model to executeduring runtime. Training processes may be run on the measurement systemor a server system that is remote from the measurement system, forinstance in a data center.

The measurement system 101 may include a waste evaluation subsystem 106to determine measurements and properties of waste added to a wastereceptacle based on signals received from sensors associated with thewaste receptacle and, in some cases, other data. For example, theevaluation subsystem may determine a change in weight in a wastereceptacle to determine a weight of waste added to a waste receptacle ata particular time, e.g., like an instance of waste being added to thewaste receptacle, or over a duration of time. In another example, theevaluation subsystem may determine a change in volume consumed by wastein the receptacle at a particular time, e.g., like an instance of wastebeing added to the waste receptacle, or over a duration of time. Inanother example, the evaluation subsystem may determine a change in odoror presence of an odor producing chemical of waste in the receptacle,e.g., like an instance of waste having an odor being added to the wastereceptacle, or over a duration of time. In another example, theevaluation subsystem may determine other changes or characteristics ofwaste add to, or within a waste receptacle. For example, an ultrasonicsensor may determine information about waste in one or more layers ofwaste within a waste receptacle. In another example, a lidar or othercomputer vision imaging sensor 103, like a camera, may determine achange in shape of a layer of waste (e.g., exposed top layer) within awaste receptacle.

The waste evaluation subsystem 106 may determine properties of wasteadded to a waste receptacle based on one or more of the abovemeasurements. In some example embodiments, the waste evaluationsubsystem 106 may executed one or more trained models to determine aclassification of waste added to a receptacle. For example, the modelmay classify, based on the measurements, properties of waste added to awaste receptacle. Example properties may include, but are not limitedto, a type or composition of waste added to a receptacle, a volume,etc., and weight of the type of waste. In some examples, a household mayinclude a plurality of different receptacles which may generally receivewaste of a particular type or composition. In some cases, differentmodels may be associated with different bins, at least by expected typesof waste. In employments where a variety of different sensor types areemployed, the models may be trained based on sensor outputs specific totheir respective bins, such as to account for differences in receptaclesize, sensor environment (e.g., lighting, position, composition of bin,etc.), among other factors. In some examples, a remote server mayinclude a waste evaluation subsystem 106 to determine information likethat described above and may return a result to the measurement system101 or the user device 110.

The waste evaluation subsystem 106 may track waste by classification,e.g., plastics-recyclable, plastics-non-recyclable, glass, cardboard,paper, bio/food waste, among other classifications, and aggregateproperties for respective waste types. Thus, for example, the evaluationsubsystem 106 may output metrics indicative of waste generated, such asper-bin, and across a collection of bins for which waste is tracked in ahousehold, and the amounts of different types (e.g., classifications) ofwaste generated. The above and other information may be stored withinwaste data 138. The data, in some examples, may be used to augmenttraining data 134 to iteratively train models to update model 132 forclassifying waste.

In some examples, the waste evaluation subsystem 106 may receiveexternal data about products consumed within a household, such asproducts ordered, or based on shopping lists, or other data sources.Product data 136 may include information about the different types ofproducts, such as amount or estimated amount of packaging waste, and insome cases may include identifiers associated with similar products orsuppliers with reduced packaging. The evaluation subsystem 106, in someexamples, may infer an amount of waste associated with a given product(e.g., packaging) based on date of receipt/pickup (e.g., groceries) orproducts at the household.

The measurement system 101 may include a sensor subsystem 108 by whichsignal data is received or captured from one or more sensors. The sensorsubsystem may request data from some sensors (e.g., poll some sensors)and obtain (e.g., receive) requests to receive transmit data (e.g.,pushed) from some sensors. In some examples, the sensor subsystem maypoll one or more sensors in response to receiving an indication fromanother sensor. For example, in response to detecting a change inweight, the sensor subsystem may poll an imaging sensor 103 to captureimaging data, or in response to detecting a change in imaging data, thesubsystem may poll a weight sensor 102. In another example, anothersensor may detect a change in a status of a receptacle (e.g., opened)and the sensor subsystem may poll or stream data from the weight andimaging sensor (or other types of sensors described herein), such asuntil a change in status (e.g., closed) of the receptacle is detected.In some examples, the sensor subsystem 108 may poll another sensor(e.g., chemical) or other type of sensor in response to the cyclingbetween states.

As noted above, the measurement system 101 may, in some examples,offload processing of certain data or operations to a remote server.Thus, for example, a remote server 105 may perform some or all of theoperations associated with the training or waste evaluation subsystem.In either case, the measurement system or remote server may transmitinformation determined about waste generated by a user or a household ofthe user to a user device, like a user computing device of the user (ora plurality of user devices associated with a household). In someexamples, the user device 110 may execute an application, like a nativeapplication, that may request and receive data via an API of themeasurement system 101 or the remote server 105. For example, the nativeapplication may receive and display metrics corresponding to differentwaste receptacles over time, current status of a receptacle (e.g., basedon current sensor output), comparison of metrics associated with theuser to other households (e.g., like other similar households based on aprofile of the user's household—like a composition of the household—andmembers thereof that spend their day at home or out of the home forwork/school, etc.). In some cases, alternative products may berecommended based on inference of waste generated from packing, oramount of a product to order based on waste generated (e.g., smallserving vs large serving) or indication that bulk or other packagingwith reduced waste is available for a particular product.

A training subsystem 104 may train one or more models, which may includea neural network, deep learning model, or other machine learning modeldescribed herein. Examples of such models may include one or more wasteclassification models or one or more encoder models. The differentmodels may be trained in different ways (separately or concurrentlythrough end-to-end training), and some models may receive inputs basedon the outputs of other models. Training of a model may compriseend-to-end training, or training of different stages (e.g., likesub-models) of a model (e.g., like a pipeline). Some examples maycombine these approaches, such as by training a model and then includingthat model within a model or as a stage of a pipeline trainedend-to-end. The training may be performed using data obtained by themeasurement system 101, waste databases 130, or user devices 110 (e.g.,like confirmation or indication of a classification and properties ofwaste), such as via one or more sensors 109, 102A-n, 103A-n or over thenetwork 150. The training subsystem 104 may store, access, or update oneor more models in various states of training from within the database130 or a local memory. For example, the training subsystem 104 mayaccess a previously trained machine learning model (or a modelundergoing training) and update the model based on newly received (orclassified data) and store an updated version of the model locally orwithin the database 130. The training subsystem 104 may access a trainedmodel to process data which in turn may be used to train another model.Thus, the training subsystem 104 may store or access data within thedatabase 130, such as data of one or more models 132 and training data134, and the training subsystem 104 may process such data to trainmodels by which feedback data 136 may be processed to generateclassifications about waste (e.g., waste data). Product data 136 may beused to further augment training data 134 for one or more models.

Some embodiments of the training subsystem 104 may train an encodermodel (e.g., a neural network, which in some examples may be anattentive neural network, like a deep learning neural network orrecurrent neural network, including or integrating an attention model)to reduce high-dimensional data, like a vector having 10,000, 100,000 or1,000,000 or more dimensions, into a latent space embedding vectorhaving significantly fewer dimensions, like 500 or fewer dimensions.Thus, for example, the dimensionality of sensor data obtained fromcomputer vision imaging sensors, like lidar or imaging cameras, or datafrom ultrasonic or other sensors, may be reduced from highdimensionality data to low dimensionality data (e.g., having orders ofmagnitude less dimensions). Some embodiments may determine pairwisedistances in the embedding space between respective pairs of thevectors. Distances may be calculated with a variety of distance metricsincluding Minkowski distance, Euclidean distance, cosine distance,Manhattan distance, and the like. Changes between successivemeasurements may be determined based on such distances, such as todetermine changes in depth, profile, or the like of waste added to awaste receptacle. A training subsystem 104 may store one or moreresulting trained models in memory to be applied to runtime problems,for instance, classification of waste added to a waste receptacle. Insome cases, a training subsystem 104 may obtain feedback (e.g.,responses) on classifications, such as from a user device 110 which maybe prompted to confirm a classification determined by a waste evaluationsubsystem when waste is added to a waste receptacle.

FIGS. 2A and 2B are example configurations for implementing a wastetracking system at the user or household level, in accordance with someembodiments. For example, as shown in FIG. 2A, a measurement system 101may incorporate a sensor 109 or communicate with one or more sensors109, or both incorporate a sensor 109 and communicate with one or moresensors. In many expected deployments, one embedded device, like ameasurement system, may incorporate the measurement system logic andother sensor devices with reduced logic may communicate with themeasurement system, e.g., like a hub. Thus, for example, one measurementsystem in a home may support a suite of sensors. FIG. 2B shows anadditional example in which a first sensor set, for example comprising aweight sensor 102A, an imaging sensor 103A and/or a sensor 109, isassociated with a first waste receptacle (e.g., BIN A), and another set,for example comprising a weight sensor 102B, an imaging sensor 103B, isassociated with a second waste receptacle (e.g., BIN B). The sensors maywirelessly transmit sensor data signals to the measurement system (e.g.,over a local network 150B, like local WiFi, Bluetooth, or other localnetwork), which may communicate with a remote server over anothernetwork (e.g., over a network 150A, like the internet). In some cases,the measurement system 101 may be wholly hosted within the cloud (e.g.,as one or more remote servers) and an intermediate hub (not shown—likean in-home assistant or smart device) may relay sensor data.

FIG. 3 illustrates example sensor configurations for tracking wasteadded to a waste receptacle at the user or household level, inaccordance with some embodiments. FIG. 3 illustrates example positioningof an example weight sensor 102, like a scale, to support exitingreceptacles of varying shapes and sizes. Different sized weight sensors102, like approximately 6″-36″ width/length square or diameter circle orrectangular having differing length and width within the example rangemay support a variety of different sized bins commonly used byhouseholds. The figure further shows an example imaging sensor 103, likea camera or lidar, that may be positioned to capture information aboutwaste placed within a bin. As noted, other sensor types may also beused. In some examples, weight sensors 102 and other sensors may beincorporated within a receptacle. For example, an outer shell mayinclude weight sensors 102, such as at a bottom or around its edge, uponwhich an inner bin rests and waste is added to the inner bin. Similarly,an imaging sensor 103 or other sensors (or multiples thereof) may beincorporated within a lid of such a receptacle. A camera may bepositioned to capture images when the lid is opened, and a lidar (e.g.,emitter and sensor) device may determine information about the contentsof the bin when the lid is closed. In some examples, a sensor may reportopen/closed state of the bin to wake sensors from a low power state toready themselves to capture data about waste added (or removed in somecases) from the bin.

FIG. 4 illustrates an example weight sensor device for capturing ameasurement of waste added to a waste receptacle at the user orhousehold level, in accordance with some embodiments. In some cases,such a weight sensor device may include the measurement system 101 logicand interface with one or more other sensors that may omit the fullsuite of such logic, e.g., with one weight sensor device associated witha first bin acting as a hub for other sensors associated with otherbins. As shown, an example weight sensor 102 may include components suchas—Computer processor-Microcontroller-analog to digital signalconverter-load cell-weight bearing surface plate/mesh designed for easyintegration-Uninterrupted power supply-Cable harness. Other sensor typesmay include cameras or lidar for computer vision, chemical sensors,ultrasonic sensor. In some cases, a hub measurement system, even whenincluding a given type of system, may include logic to process datareceived from other sensor types, such as volumetric estimationalgorithms and the like.

In some examples, the system is custom designed to fit seamlessly intothe customer's household. Example systems may include modular designs tofit various household configurations. Example systems may support 0 upto 1000 pound bins. Example systems in the may be powered independentlyby a dedicated rechargeable battery source. Example system may include aportable sensor (or sensors) to collect data outside the home (e.g.,office, travel) and may report that data e.g., periodically, to a remoteserver in association with the user or a user device of the user.Example systems may support household composting and recycling of waste.Example systems may be integrated into interiors supplied and fitted bythird parties. Example systems may estimate waste reduction to CO2emission savings based on determined classifications and amounts ofwaste relative to past periods or based on selections of alternativeproduce or packaged goods. Example systems may determine a scoringmatrix, and include a parameter for calculating total impact on globalwaste reduction.

FIG. 5 is an example machine learning model in accordance with someembodiments. As an example, described with respect to FIG. 5 , a machinelearning model 300 may take one or more inputs and generate one or moreoutputs. Training records 302 may be used in training the machinelearning model 300. Examples of a machine learning model 300 may includea neural network or other machine learning model described herein, maytake inputs 304 (e.g., input data that described above) and provideoutputs 306 (e.g., output data like that described above) based on theinputs and parameter values of the model. For example, the model 300 maybe fed an input or set of inputs 304 for processing based on sensoroutputs and other data or outputs determined by other models and providean output or set of outputs 306. In some cases, outputs 306 may be fedback to machine learning model 300 as input to train machine learningmodel 300 (e.g., alone or in conjunction with indications of theperformance of outputs 306, thresholds associated with the inputs, orwith other feedback information). In another use case, machine learningmodel 300 may update its configurations (e.g., weights, biases, or otherparameters) based on its assessment of a prediction or instructions(e.g., outputs 306) against feedback information (e.g., receivedclassification confirmations or inferred classification confirmations)or outputs of other models (e.g., classification scores). In another usecase, such as where machine learning model 300 is a neural network,connection weights may be adjusted to reconcile differences between theneural network's prediction or instructions and the feedback. In afurther use case, one or more neurons (or nodes) of the neural networkmay require that their respective errors are sent backward through theneural network to them to facilitate the update process (e.g.,backpropagation of error). Updates to the connection weights may, forexample, be reflective of the magnitude of error propagated backwardafter a forward pass has been completed. In this way, for example, themachine learning model 300 may be trained to generate better predictionsor instructions.

In some embodiments, the machine learning model 300 may include anartificial neural network. In such embodiments, machine learning model300 may include an input layer and one or more hidden layers. Eachneural unit of the machine learning model may be connected with one ormore other neural units of the machine learning model 300. Suchconnections can be enforcing or inhibitory in their effect on theactivation state of connected neural units. Each individual neural unitmay have a summation function which combines the values of one or moreof its inputs together. Each connection (or the neural unit itself) mayhave a threshold function that a signal must surpass before itpropagates to other neural units. The machine learning model 300 may beself-learning or trained, rather than explicitly programmed, and mayperform significantly better in certain areas of problem solving, ascompared to computer programs that do not use machine learning. Duringtraining, an output layer of the machine learning model 300 maycorrespond to a classification, and an input known to correspond to thatclassification may be input into an input layer of machine learningmodel during training. During testing, an input without a knownclassification may be input into the input layer, and a determinedclassification may be output. In some examples, a classification may bean indication of whether a selection of samples is predicted to optimizean objective function that balances between exploration of a semanticspaces and optimization of convergence in explored areas. In someexamples, a classification may be an indication of a type and amount ofwaste added to a receptacle, such as based on a vector indicative of oneor more sensor outputs obtained in association with the addition of thewaste to the receptacle.

In some embodiments, a machine learning model 300 may be structured as afactorization machine model. A machine learning model 300 may be anon-linear model or supervised learning model that can performclassification or regression. For example, the machine learning model300 may be a general-purpose supervised learning algorithm that a systemuses for both classification and regression tasks. Alternatively, themachine learning model 300 may include a Bayesian model configured toperform variational inference (e.g., deviation or convergence) of aninput from previously processed data (or other inputs in a set ofinputs). A machine learning model 300 may be implemented as a decisiontree or as an ensemble model (e.g., using random forest, bagging,adaptive booster, gradient boost, XGBoost, etc.). In some embodiments, amachine learning model 300 may incorporate one or more linear models bywhich one or more features are pre-processed or outputs arepost-processed, and training of the model may comprise training with orwithout pre or post-processing by such models.

In some embodiments, a machine learning model 300 implements deeplearning via one or more neural networks, one or more of which may be arecurrent neural network. Some embodiments may reduce dimensionality ofhigh-dimensional data (e.g., with one million or more dimensions) beforeit is provided to a model, such as by forming latent space embeddingvectors (e.g., with 500 or fewer dimensions) based on high dimensiondata. In some embodiments, the high-dimensional data may be reduced byan encoder model (which may implement a neural network) that processesvectors or other data output by one or more sensors. For example,training of a machine learning model 300 may include the generation of aplurality of latent space embeddings as, or in connection with, outputs306 of the model which may be classified (e.g., for additions of wasteto a waste receptacle).

Examples of machine learning model may include multiple models. Forexample, a clustering model may cluster latent space embeddingsrepresented in training (or output) data. In some cases, classificationof a (or a plurality of) latent space embedding within a cluster mayindicate information about other latent space embeddings within, orwhich are assigned to the cluster. For example, a clustering model(e.g., K-means, DBSCAN (density-based spatial clustering of applicationswith noise), or a variety of other unsupervised machine learning modelsused for clustering) may take as input a latent space embedding anddetermine whether it belongs (e.g., based on a threshold distance) toone or more other clusters of other space embeddings that have beenpreviously trained. In some examples, a representative embedding for acluster of embeddings may be determined, such as via one or moresamplings of the cluster to obtain rankings by which the representativeembedding may be selected, and that representative embedding may besampled (e.g., more often) for ranking against other embeddings not inthe cluster or representative embeddings of other clusters.

FIG. 6A is a flowchart of an example process for determining propertiesabout waste added to a waste receptacle to track waste generation, inaccordance with some example embodiments, which may be executed withdevices like those described in the context of FIGS. 1-4 . The processmay comprise the steps:

-   -   Detect New Waste 601;    -   Obtain Measurement(s) from Sensor(s) 603;    -   Analyze Measurement(s) 605;    -   Determine Properties of Waste 607;    -   Catalog Properties of Waste 609;    -   BIN Emptied? 611; (If No return to step 601, if yes proceed to        step 613)    -   BIN Report 613.

FIG. 6B is a flowchart of an example process for reporting on trackedwaste generation across one or more waste receptacles, in accordancewith some example embodiments, which may be executed with devices likethose described in the context of FIGS. 1-4 . The process may comprisethe steps:

-   -   Catalog Properties of Waste 651;    -   Prompt User? 653;    -   Aggregate Properties of Waste Types 655;    -   BIN Report 657;    -   Aggregate BIN Reports for Multiple Tracked BINs? 659;    -   Update Household Waste Report 661.

FIGS. 7A and 7B illustrate an example user interface for registeringsensors in association with a receptacle for tracking waste generated,in accordance with some embodiments, which may be generated to associateone or more sensors like those described in the context of FIGS. 1-4with a waste receptacle.

FIG. 7C illustrates example user interfaces for reporting propertiesdetermined about generated waste added to waste receptacles, inaccordance with some embodiments, which may be generated to displaymetrics indicative of types of waste generated by a household based onclassifications and properties of waste determined by configurationslike those described in the context of FIGS. 1-4 with a wastereceptacle.

FIG. 8 is a physical architecture block diagram that shows an example ofa computing device (or data processing system) by which some aspects ofthe above techniques may be implemented. Various portions of systems andmethods described herein, may include or be executed on one or morecomputer systems similar to computing system 1000. Further, processesand modules or subsystems described herein may be executed by one ormore processing systems similar to that of computing system 1000.

Computing system 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to system memory 1020, an input/outputI/O device interface 1030, and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1020). Computing system 1000 may be a uni-processor systemincluding one processor (e.g., processor 1010 a), or a multi-processorsystem including any number of suitable processors (e.g., 1010 a-1010n). Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus may also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1000may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computer system 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computer system 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computer system 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputer system 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computer system 1000 to a network. Network interface 1040may facilitate data exchange between computer system 1000 and otherdevices connected to the network. Network interface 1040 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1010 a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). Instructions or other program code toprovide the functionality described herein may be stored on a tangible,non-transitory computer readable media. In some cases, the entire set ofinstructions may be stored concurrently on the media, or in some cases,different parts of the instructions may be stored on the same media atdifferent times.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, system memory 1020, network interface 1040,I/O devices 1060, and/or other peripheral devices. I/O interface 1050may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., system memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1000 or multiple computer systems1000 configured to host different portions or instances of embodiments.Multiple computer systems 1000 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1000 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1000 may be transmitted to computer system1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending, or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present techniques may be practiced with othercomputer system configurations.

FIG. 9 is a flow chart illustrating method 900 according to the presentinvention. In FIG. 9 , optional steps in method 900 are shown in dottedboxes. The flow in method 900 flows from the start oval to operation910, which indicates to receive data from a household device. The dataincludes at least a first amount of a first type of waste. Fromoperation 910, the flow in method 900 proceeds to operation 920, whichindicates to compare the data with a household profile includinghistorical waste data from the household. From operation 920, the flowin method 900 proceeds to operation 930, which indicates to sendnotifications to the household device based on the comparing operation.The notifications include information relating to the data and thehousehold profile, and the household device is configured to communicatethe notifications to a household member. From operation 930, the flow inmethod 900 proceeds to optional operation 940, which indicates toreceive first goals from the household device. From optional operation940, the flow in method 900 proceeds to optional operation 950, whichindicates to determine second goals based on community waste averages.From optional operation 950, the flow in method 900 proceeds to optionaloperation 960, which indicates to send recommendations to the householddevice based at least in part on the data and the household profile.From optional operation 960, the flow in method 900 proceeds to the endoval.

In block diagrams, illustrated components are depicted as discretefunctional blocks, but embodiments are not limited to systems in whichthe functionality described herein is organized as illustrated. Thefunctionality provided by each of the components may be provided bysoftware or hardware modules that are differently organized than ispresently depicted, for example such software or hardware may beintermingled, conjoined, replicated, broken up, distributed (e.g. withina data center or geographically), or otherwise differently organized.The functionality described herein may be provided by one or moreprocessors of one or more computers executing code stored on a tangible,non-transitory, machine readable medium. In some cases, notwithstandinguse of the singular term “medium,” the instructions may be distributedon different storage devices associated with different computingdevices, for instance, with each computing device having a differentsubset of the instructions, an implementation consistent with usage ofthe singular term “medium” herein. In some cases, third party contentdelivery networks may host some or all of the information conveyed overnetworks, in which case, to the extent information (e.g., content) issaid to be supplied or otherwise provided, the information may providedby sending instructions to retrieve that information from a contentdelivery network.

The reader should appreciate that the present application describesseveral independently useful techniques. Rather than separating thosetechniques into multiple isolated patent applications, applicants havegrouped these techniques into a single document because their relatedsubject matter lends itself to economies in the application process. Butthe distinct advantages and aspects of such techniques should not beconflated. In some cases, embodiments address all of the deficienciesnoted herein, but it should be understood that the techniques areindependently useful, and some embodiments address only a subset of suchproblems or offer other, unmentioned benefits that will be apparent tothose of skill in the art reviewing the present disclosure. Due to costsconstraints, some techniques disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionsections of the present document should be taken as containing acomprehensive listing of all such techniques or all aspects of suchtechniques.

It should be understood that the description and the drawings are notintended to limit the present techniques to the particular formdisclosed, but to the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the present techniques as defined by the appended claims.Further modifications and alternative embodiments of various aspects ofthe techniques will be apparent to those skilled in the art in view ofthis description. Accordingly, this description and the drawings are tobe construed as illustrative only and are for the purpose of teachingthose skilled in the art the general manner of carrying out the presenttechniques. It is to be understood that the forms of the presenttechniques shown and described herein are to be taken as examples ofembodiments. Elements and materials may be substituted for thoseillustrated and described herein, parts and processes may be reversed oromitted, and certain features of the present techniques may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the present techniques.Changes may be made in the elements described herein without departingfrom the spirit and scope of the present techniques as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Similarly, reference to “a computer system”performing step A and “the computer system” performing step B mayinclude the same computing device within the computer system performingboth steps or different computing devices within the computer systemperforming steps A and B. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X′editems,” used for purposes of making claims more readable rather thanspecifying sequence. Statements referring to “at least Z of A, B, andC,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Zof the listed categories (A, B, and C) and do not require at least Zunits in each category. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.Features described with reference to geometric constructs, like“parallel,” “perpendicular/orthogonal,” “square”, “cylindrical,” and thelike, should be construed as encompassing items that substantiallyembody the properties of the geometric construct, e.g., reference to“parallel” surfaces encompasses substantially parallel surfaces. Thepermitted range of deviation from Platonic ideals of these geometricconstructs is to be determined with reference to ranges in thespecification, and where such ranges are not stated, with reference toindustry norms in the field of use, and where such ranges are notdefined, with reference to industry norms in the field of manufacturingof the designated feature, and where such ranges are not defined,features substantially embodying a geometric construct should beconstrued to include those features within 15% of the definingattributes of that geometric construct. The terms “first”, “second”,“third,” “given” and so on, if used in the claims, are used todistinguish or otherwise identify, and not to show a sequential ornumerical limitation. As is the case in ordinary usage in the field,data structures and formats described with reference to uses salient toa human need not be presented in a human-intelligible format toconstitute the described data structure or format, e.g., text need notbe rendered or even encoded in Unicode or ASCII to constitute text;images, maps, and data-visualizations need not be displayed or decodedto constitute images, maps, and data-visualizations, respectively;speech, music, and other audio need not be emitted through a speaker ordecoded to constitute speech, music, or other audio, respectively.Computer implemented instructions, commands, and the like are notlimited to executable code and may be implemented in the form of datathat causes functionality to be invoked, e.g., in the form of argumentsof a function or API call. To the extent bespoke noun phrases (and othercoined terms) are used in the claims and lack a self-evidentconstruction, the definition of such phrases may be recited in the claimitself, in which case, the use of such bespoke noun phrases should notbe taken as invitation to impart additional limitations by looking tothe specification or extrinsic evidence.

In this patent, to the extent any U.S. patents, U.S. patentapplications, or other materials (e.g., articles) have been incorporatedby reference, the text of such materials is only incorporated byreference to the extent that no conflict exists between such materialand the statements and drawings set forth herein. In the event of suchconflict, the text of the present document governs, and terms in thisdocument should not be given a narrower reading in virtue of the way inwhich those terms are used in other materials incorporated by reference.

What is claimed is:
 1. A method comprising: receiving data from adevice, the data comprising at least a first amount of a first type ofwaste; comparing the data with a profile including historical waste datafrom a location; and sending notifications to the device based on thecomparing operation, the notifications including information relating tothe data and the profile, the device configured to communicate thenotifications to a user.
 2. The method of claim 1, further comprising:receiving first goals from the device; and determining second goalsbased on community waste averages; wherein the profile further includesthe first goals and the second goals.
 3. The method of claim 2, wherein:the comparing comprises comparing the first amount to the first goal,the notifications including information tracking a first progresstowards the first goal; the comparing further comprises comparing thefirst amount to the second goal, the notifications including informationtracking a second progress towards the second goal; and thenotifications include at least one of positive feedback prompts forwaste reduction and awards for waste reduction.
 4. The method of claim1, wherein the historical waste data includes a daily average, and thecomparing operation includes comparing the data and the daily average.5. The method of claim 1, further comprising: sending recommendations tothe device based at least in part on the data and the profile, thedevice being configured to communicate the notifications and therecommendations to the user by at least one of a display, a speaker, anda wireless connection to a mobile device; and sending the notificationsto an application running on a mobile device of the user.
 6. A devicecomprising: at least one sensor associated with a waste receptacle, theat least one sensor configured to determine properties of waste added tothe waste receptacle; a processor configured to receive measurement datafrom the at least one sensor and configured to analyze the measurementdata; and at least one of a display, a speaker, and a wirelessconnection to a mobile device configured to provide information to auser, the information being based on the measurement data obtained fromthe at least one sensor associated with the waste receptacle.
 7. Thedevice of claim 6, wherein the information comprises categorizationnotifications, the categorization notifications indicating whether wasteplaced in the waste receptacle is one of: properly placed wholly orpartially in another receptacle; and compostable.
 8. The device of claim6, wherein the device is configured to transmit to a remote server atleast one of: the measurement data from the at least one sensor; and aresult from the processor analyzing the measurement data.
 9. The deviceof claim 6, wherein the at least one sensor is configured tocontinuously capture the measurement data and is at least one of: aweight sensor; a computer vision imaging sensor; a visual sensor; anultrasonic sensor; and a chemical sensor.
 10. The device of claim 9,wherein the at least one sensor is the computer vision imaging sensor,and the computing vision imaging sensor includes a processor running anartificial intelligence trained on further measurement data obtainedfrom at least one further sensor associated with at least one furtherwaste receptacle.
 11. A system comprising: a receiving module configuredto receive data from a location, the data being obtained from a sensormeasuring a first amount of a first type of waste; a processorconfigured to receive the data from the receiving module and process thedata; and a sending module configured to send notifications to a userdevice, the notifications including information relating to the firstamount, the user device configured to display the notifications to auser.
 12. The system of claim 11, wherein the processor is furtherconfigured to classify the waste based on a statistical analysis models.13. The system of claim 11, wherein: the data are measurements of thewaste, the measurements being at least one of a weight, a volume, and acomposition; and the processor determines amounts of different types ofwaste generated.
 14. The system of claim 13, wherein the processor isfurther configured to update a machine learning model for identifyingthe different types of waste generated based on the measurements. 15.The system of claim 11, wherein: the receiving module is configured toreceive second data from a second location, the second data beingobtained from a second sensor measuring a second amount of the firsttype of waste; the processor is configured to receive the second datafrom the receiving module and process the second data and compare thefirst data and the second data; and the sending module is configured tosend second notifications to a second user device, the secondnotifications including further information relating to the secondamount, the second user device configured to display the secondnotifications to a second user; wherein the notification furtherincludes at least one of an award, an indication of community support, afirst comparison of the first amount and a community waste metric, and asecond comparison of a daily average of the location and a communitydaily average.